Urban inland inundation toughness evaluation method based on multi-body and system dynamics coupling modeling

Abstract

The invention discloses an urban inland inundation toughness evaluation method based on multi-subject and system dynamics coupling modeling, which is applied to the technical field of urban water affairs, disaster prevention and reduction and emergency research, and comprises the following steps: acquiring various parameter values of urban inland inundation formation factors, determining subject objects, and performing mathematical expression of abstract behaviors according to attribute characteristics to obtain an urban inland inundation toughness evaluation result; obtaining a behavior decision result; positive and negative feedback relations among various indexes of the system dynamics model are confirmed, a behavior decision result is applied to the model, interactive simulation of microscopic decision and macroscopic phenomena is carried out, and an urban inland inundation toughness evaluation coupling model is obtained; wherein the system dynamics model comprises a social toughness subsystem, an economic toughness subsystem, a facility toughness subsystem and an environment toughness subsystem; and based on the urban inland inundation toughness evaluation coupling model, designing different scenario policies, and exploring an optimal management scheme influencing urban inland inundation toughness construction. According to the invention, weak links in urban waterlogging disaster coping can be accurately identified.

Description

Technical Field

[0001] The present invention relates to the field of urban water affairs, disaster prevention and mitigation, and emergency research technology, and in particular to an urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling. Background Art

[0002] Against the backdrop of global warming, extreme weather events are occurring frequently. The operation of cities generates a large amount of heat from daily life and industry, which causes atmospheric instability, leading to the urban heat island effect and rain island effect, increasing the uncertainty and possibility of urban rainstorms. With the development of urbanization, urban areas are constantly expanding, and the risk of urban waterlogging is increasing. Therefore, strengthening the resilience of cities to resist disasters and reduce risks is an important goal at present.

[0003] Urban waterlogging resilience reflects the city's ability to adapt and recover from waterlogging disasters, that is, when the urban system is hit by waterlogging disasters, it can maintain basic vitality, minimize losses, and quickly adapt, recover and develop. Urban waterlogging has the characteristics of short duration and spatial distribution. It is a phenomenon caused by multiple factors in both human activities and the physical environment. Human activities include social and economic factors such as emergency management measures for waterlogging events and urban maintenance capital investment. The physical environment includes natural conditions and urban facility conditions such as heavy rainfall events, land cover types, drainage network construction, and building density. The stronger the city's ability to adapt and recover from waterlogging events, the higher the level of waterlogging resilience construction. Reducing the risk probability of urban waterlogging and reducing the losses caused by waterlogging are the main goals of urban waterlogging resilience construction. At present, the research on improving urban waterlogging resilience at home and abroad includes two aspects, namely, the research on non-engineering measures, improving the government's joint prevention and control technology, and enhancing residents' risk and disaster perception; the research on engineering measures, such as low-impact development technology, water-sensitive urban design, sponge city construction technology, through increasing drainage pipe networks, building rain gardens and other means, with the help of hydrodynamic models, GIS technology and linear weighted combination to draw dangerous prone maps. By evaluating the level of waterlogging resilience construction, the weak links of cities in responding to waterlogging disasters can be identified, providing a scientific basis for policy making, thereby improving the city's disaster adaptation and recovery capabilities, helping cities to rationally plan and optimize resource allocation, and building high-standard and resilient urban systems.

[0004] Therefore, how to provide an urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling that can comprehensively consider the influencing factors of urban waterlogging and the causal relationship between the influencing factors, realize scientific and refined urban waterlogging resilience assessment, accurately identify the weak links in urban waterlogging disaster response, and provide a scientific basis for policy making, thereby improving the city's disaster adaptability and recovery capabilities, helping cities to rationally plan and optimize resource allocation, and build high-standard and resilient urban systems is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] In view of this, this paper proposes an urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling. From the perspective of combining micro and macro, the level of urban waterlogging resilience construction is evaluated, and by setting different policy scenarios, the important factors and weak links that affect the adaptability and recovery capacity of urban waterlogging are explored, providing government decision makers with a more sophisticated urban waterlogging resilience construction management plan.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for assessing urban waterlogging resilience based on multi-agent and system dynamics coupled modeling, including:

[0008] Step 1: Obtain the parameter values ​​of the factors that cause urban waterlogging, determine the main object of the urban waterlogging resilience construction problem, and mathematically express the abstract behavior based on the attribute characteristics of the main object to obtain the behavioral decision-making result of the main object;

[0009] Step 2: Confirm the positive and negative feedback relationship between various indicators in the system dynamics model of urban waterlogging resilience construction level, and apply the behavioral decision-making results to the system dynamics model to simulate the interaction between micro-decision-making and macro-phenomena to obtain the urban waterlogging resilience assessment coupling model; the system dynamics model includes: social resilience subsystem, economic resilience subsystem, facility resilience subsystem, and environmental resilience subsystem;

[0010] Step 3: Conduct sensitivity tests on the coupled model for urban waterlogging resilience assessment to identify the important factors that affect urban waterlogging resilience construction, and explore the best management solutions that affect urban waterlogging resilience construction by designing different scenario policies and analyzing the assessment results of different scenarios.

[0011] Optionally, in step 1, a mathematical expression of the abstract behavior is performed according to the attribute characteristics of the subject object, specifically:

[0012] The subject objects are classified according to their attribute characteristics, and the initial states and behavior rules of the subject objects of different attribute classifications are determined in turn. Statistical analysis of the decision-making of the subject objects is performed to obtain simulation results based on multiple subjects.

[0013] Optionally, in step 2, the positive and negative feedback relationships between the indicators in the system dynamics model of urban waterlogging resilience construction level are confirmed, and the behavioral decision results are applied to the system dynamics model to perform interactive simulation of micro-decision-making and macro-phenomena, specifically:

[0014] Determine the positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationship between each indicator factor, set the simulation results of multiple subjects to the mathematical functions related to the factors in the system dynamics model, perform model optimization tests based on historical data, and set the simulation results based on the system dynamics model to the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro decision-making and macro phenomena.

[0015] Optionally, in step 3, a sensitivity test is performed on the urban waterlogging resilience assessment coupling model to identify the important factors affecting the construction of urban waterlogging resilience, specifically:

[0016] By adjusting the behavioral rules of the main objects and the parameters of the subsystem model in the system dynamics model, the future changes in urban waterlogging resilience construction are predicted and the important factors affecting urban waterlogging resilience construction are analyzed.

[0017] Optionally, in step 3, by designing different scenario policies and analyzing the assessment results of different scenarios, the best management solutions that affect urban waterlogging resilience construction are explored, specifically:

[0018] Based on the existing parameter values, predict the changing trend of urban waterlogging resilience;

[0019] Based on the existing historical data, by modifying the parameter values ​​in the mathematical function or modifying the behavioral design to set different scenario policies, the changing trends of urban waterlogging resilience in different scenarios are compared and analyzed, and suggestions are put forward for improving urban waterlogging resilience construction;

[0020] Take practical measures based on the changing trends and suggestions to improve urban waterlogging resilience, thereby reducing the economic losses caused by waterlogging and improving the level of urban resilience.

[0021] It can be seen from the above technical solutions that, compared with the prior art, the present invention proposes a method for evaluating urban waterlogging resilience based on multi-agent and system dynamics coupling modeling. Through the coupling modeling of dynamic evolution of micro-agents based on multi-agents and macro-level based on system dynamics models, the influencing factors of urban waterlogging and the causal relationship between the influencing factors can be fully considered in the urban waterlogging resilience assessment, and a scientific and refined urban waterlogging resilience assessment can be achieved, and the weak links of the city in responding to waterlogging disasters can be accurately identified, providing a scientific basis for policy making, thereby improving the city's disaster adaptability and recovery capabilities, helping cities to rationally plan and optimize resource allocation, and build high-standard, resilient urban systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0024] Figure 2 This is a schematic diagram of the coupling model of the present invention.

[0025] Figure 3 It is a schematic diagram of the urban waterlogging resilience coupling model of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiment 1:

[0028] Embodiment 1 of the present invention discloses a method for evaluating urban waterlogging resilience based on multi-agent and system dynamics coupling modeling. Figure 1 As shown, including:

[0029] Step 1: Obtain the parameter values ​​of the factors that cause urban waterlogging, determine the main object of the urban waterlogging resilience construction problem, and mathematically express the abstract behavior based on the attribute characteristics of the main object to obtain the behavioral decision-making result of the main object.

[0030] Multi-agent based modeling, also known as agent-based modeling (ABM), is a computational model based entirely on individual behavior at the micro level. At the micro level, agents with different attribute characteristics and individual behaviors are used as agents to design behaviors under different conditions to simulate the decision-making of micro-agents and the resulting group phenomena. Through the behavioral interactions between micro-agents, complex phenomena at the macro level emerge intuitively. This method has been applied to many fields, such as urban solid waste management, transportation, the spread of infectious diseases, and almost all types of disaster evacuations, to achieve long-term planning management or real-time emergency management measures.

[0031] In view of the problem of urban waterlogging, based on multi-agent modeling, the important factors affecting the construction of urban waterlogging resilience at the micro level are studied. Taking the period of waterlogging events as the simulation period, the real-time emergency management of urban waterlogging is modeled. The model framework based on residents with different vulnerable attributes and evacuation roads with different congestion levels can provide support for urban communities to formulate better emergency evacuation management; by setting different proportions of crowd decision makers, crowd evacuation response speeds, and the number of people accommodated in shelters, the cascading impact mechanism of waterlogging disasters on crowd behavior characteristics and urban emergency evacuation is studied, providing a decision-making basis for emergency response to urban waterlogging. Due to the different attitudes and reaction times of people to disaster warnings, a modeling framework based on agents was developed to evaluate the impact of two factors, namely, the behavioral response of residents to warning responses and the density of residential areas, on the warning effect. The results show that in high-density residential areas, the benefits of disaster warnings are significantly limited, and emphasize the importance of considering the heterogeneity of human behavior when simulating warning response systems.

[0032] The time step of years can be used to study long-term planning measures to alleviate urban waterlogging. By modeling the government and households with different attributes, the government behavior of proactively responding to disasters and the household behavior of rationally taking flood prevention measures can be simulated, which has a significant effect on reducing the annual losses caused by floods. Simulation modeling with other participants as the main body can provide support for the government to coordinate with all sectors of society to deal with waterlogging. With insurance companies as the main body, according to the impact mechanism of disaster insurance policies on flood risks, the behavior of adjusting insurance premiums, insurance coverage, etc. is designed, and the optimal insurance policy in flood disaster management is analyzed, providing important support for local governments to alleviate disaster losses; with real estate developers as the main body, the housing prices in the waterlogging disaster area are adjusted, and the migration behavior of households with different income types and different attitudes towards disasters is simulated. Combined with the number of households inside and outside the flood area under different scenarios, the utility value of housing is calculated, and developers can reasonably adjust the housing prices in different risk areas.

[0033] The affected residents and government departments, such as the Ministry of Emergency Management, the Ministry of Transport, the Health Commission, and other relevant participants, such as online media and enterprises, can be used as micro-subjects of urban waterlogging for multi-agent simulation modeling. Starting from the managers or participants of waterlogging, changing behaviors will greatly help improve the resilience of urban waterlogging by implementing waterlogging management policies efficiently and reducing losses caused by waterlogging.

[0034] The mathematical expression of abstract behavior is based on the attribute characteristics of the subject object, specifically:

[0035] The subject objects are classified according to their attribute characteristics, and the initial states and behavior rules of the subject objects of different attribute classifications are determined in turn. Statistical analysis of the decision-making of the subject objects is performed to obtain simulation results based on multiple subjects.

[0036] Step 2: Confirm the positive and negative feedback relationship between various indicators in the system dynamics model of urban waterlogging resilience construction level, and apply the behavioral decision-making results to the system dynamics model to conduct interactive simulation of micro-decision-making and macro-phenomena to obtain the urban waterlogging resilience assessment coupling model; among them, the system dynamics model includes: social resilience subsystem, economic resilience subsystem, facility resilience subsystem, and environmental resilience subsystem.

[0037] The System Dynamics Model (SDM) consists of inventory, flow, variables and feedback loops. It is based on feedback control theory and was founded by Forrester of MIT in 1956. It has become an independent and complete discipline. This method combines the control principle of information feedback with the logic of causality to establish a simulation model of the system. There is an indirect influence between the various factors, not a single linear relationship. This model is mostly used to deal with complex system problems at the macro level and explore the dynamic evolution process of the system, such as urban resource management, environmental issues, regional economic evolution, population forecasting, land use type evolution, etc.

[0038] In view of the complexity of urban waterlogging, the system dynamics model can realize the quantitative simulation and trend assessment of urban waterlogging problems at the macro level. Based on SLR, the literature was screened to extract the indicators of the urban waterlogging disaster risk system, and the urban waterlogging disaster system structure consisting of four parts: disaster-causing factors, disaster-pregnant environment, disaster-bearing body, and disaster prevention and mitigation was established. By combining the multi-agent system simulation framework theory, a system dynamics model of urban rainstorm flood resilience under different rainstorm scenarios was established, and the complex structure and dynamic behavior of the urban rainstorm flood resilience system were quantitatively analyzed to provide a reference for the construction of resilient cities.

[0039] The system dynamics model studies urban waterlogging problems, taking into account a more comprehensive range of indicators and factors. There is a causal relationship between the research indicators, forming a positive and negative feedback structure, making the macro-estimated evolution process more scientific and highly credible.

[0040] Both multi-agent-based modeling and system dynamics modeling are classic simulation modeling technologies, but multi-agent-based modeling is the modeling of micro-agents, while system dynamics modeling is mainly used to explore the dynamic evolution at the macro level. ABM has the advantages of good scalability, flexibility, and convenient interaction, while SDM has the characteristics of clear factor correlation and nonlinearity. Combining the micro and macro levels, it can more deeply explore the weak links of system engineering problems and help to propose more efficient and sophisticated management solutions.

[0041] Urban waterlogging is a complex phenomenon caused by multiple factors, which is the result of the interaction between the physical environment and social activities. In the problem of urban waterlogging, there is an interaction between the adaptive behavior of the government, residents and others and the urban environment with the risk of waterlogging caused by heavy rains, and the two influence each other. At present, the research methods of urban waterlogging lack the overall research of social behavior and physical environment. By combining the micro and subjective, the mutual influence mechanism and main influencing factors of urban waterlogging problems are explored. Combining the behavioral methods of social sciences with the quantitative models of natural sciences, the behaviors dominated by micro-subjects can be adapted to the external macro changes, and the risks of urban waterlogging can be reduced in a refined manner, and the losses of urban waterlogging can be reduced.

[0042] Confirm the positive and negative feedback relationship between various indicators in the system dynamics model of urban waterlogging resilience construction level, and apply the behavioral decision-making results to the system dynamics model to simulate the interaction between micro-decision-making and macro-phenomena, such as Figure 2 As shown, specifically:

[0043] Determine the positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationship between each indicator factor, set the simulation results of multiple subjects to the mathematical functions related to the factors in the system dynamics model, perform model optimization tests based on historical data, and set the simulation results based on the system dynamics model to the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro decision-making and macro phenomena.

[0044] Taking government departments and residents as two micro-subjects, and the economic resilience subsystem, social resilience subsystem, facility resilience subsystem, and environmental resilience subsystem as the four subsystems of the system dynamics model, a coupling model for urban waterlogging resilience assessment is established. Figure 3 shown.

[0045] Step 3: Conduct sensitivity tests on the coupled model for urban waterlogging resilience assessment to identify the important factors that affect urban waterlogging resilience construction, and explore the best management solutions that affect urban waterlogging resilience construction by designing different scenario policies and analyzing the assessment results of different scenarios.

[0046] The sensitivity test of the coupled model for urban waterlogging resilience assessment was conducted to identify the important factors affecting urban waterlogging resilience construction, specifically:

[0047] By adjusting the behavioral rules of the main objects and the parameters of the subsystem model in the system dynamics model, the future changes in urban waterlogging resilience construction are predicted and the important factors affecting urban waterlogging resilience construction are analyzed.

[0048] By designing different scenario policies and analyzing the evaluation results of different scenarios, we explore the best management solutions that affect urban waterlogging resilience construction, specifically:

[0049] Based on the existing parameter values, predict the changing trend of urban waterlogging resilience;

[0050] Based on the existing historical data, by modifying the parameter values ​​in the mathematical function or modifying the behavioral design to set different scenario policies, the changing trends of urban waterlogging resilience in different scenarios are compared and analyzed, and suggestions are put forward for improving urban waterlogging resilience construction;

[0051] Take practical measures based on the changing trends and suggestions to improve urban waterlogging resilience, thereby reducing the economic losses caused by waterlogging and improving the level of urban resilience.

[0052] The present invention can also provide methodological support for other types of disasters.

[0053] The embodiment of the present invention discloses a method for evaluating urban waterlogging resilience based on multi-agent and system dynamics coupling modeling. Through the coupling modeling of dynamic evolution of micro-agents based on multi-agents and macro-level based on system dynamics models, the influencing factors of urban waterlogging and the causal relationship between the influencing factors can be fully considered in the urban waterlogging resilience assessment, and a scientific and refined urban waterlogging resilience assessment can be achieved, and the weak links of the city in responding to waterlogging disasters can be accurately identified, providing a scientific basis for policy making, thereby improving the city's disaster adaptability and recovery capabilities, helping cities to rationally plan and optimize resource allocation, and building high-standard and resilient urban systems.

[0054] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0055] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing urban waterlogging resilience based on multi-agent and system dynamics coupling modeling, characterized in that: include: Step 1: Obtain the parameter values ​​of the factors that cause urban waterlogging, determine the main object of the urban waterlogging resilience construction problem, and mathematically express the abstract behavior based on the attribute characteristics of the main object to obtain the behavior decision result of the main object; Step 2: Confirm the positive and negative feedback relationship between various indicators in the system dynamics model of urban waterlogging resilience construction level, and apply the behavioral decision results to the system dynamics model to conduct interactive simulation of micro-decision-making and macro-phenomenon to obtain the urban waterlogging resilience assessment coupling model; wherein the system dynamics model includes: social resilience subsystem, economic resilience subsystem, facility resilience subsystem, and environmental resilience subsystem; Step 3: Conduct sensitivity tests on the urban waterlogging resilience assessment coupling model to identify the important factors affecting urban waterlogging resilience construction, and explore the best management solutions that affect urban waterlogging resilience construction by designing different scenario policies and analyzing the assessment results of different scenarios.

2. The urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling according to claim 1 is characterized in that: In step 1, the mathematical expression of the abstract behavior is performed according to the attribute characteristics of the subject object, specifically: The subject objects are classified according to their attribute characteristics, and the initial states and behavior rules of the subject objects of different attribute classifications are determined in turn, and decision statistical analysis of the subject objects is performed to obtain simulation results based on multiple subjects.

3. The urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling according to claim 2 is characterized in that: In step 2, the positive and negative feedback relationships between the indicators in the system dynamics model of urban waterlogging resilience construction level are confirmed, and the behavioral decision results are applied to the system dynamics model to perform interactive simulation of micro-decision-making and macro-phenomena, specifically: Determine the positive and negative feedback relationships between different subsystem models in the system dynamics model and the mathematical function relationship between each indicator factor, set the simulation results of multiple subjects to the mathematical functions related to the factors in the system dynamics model, perform model optimization inspection based on historical data, and set the simulation results based on the system dynamics model to the relevant behavioral states after the subject object attribute classification, so as to realize the interactive simulation of micro decision-making and macro phenomena.

4. The urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling according to claim 1 is characterized in that: In step 3, the urban waterlogging resilience assessment coupling model is subjected to a sensitivity test to identify the important factors affecting the construction of urban waterlogging resilience, specifically: By adjusting the behavioral rules of the main object and the parameters of the subsystem model in the system dynamics model, the future changes in urban waterlogging resilience construction are predicted and the important factors affecting urban waterlogging resilience construction are analyzed.

5. The urban waterlogging resilience assessment method based on multi-agent and system dynamics coupling modeling according to claim 1 is characterized in that: In step 3, different scenario policies are designed and analyzed based on the assessment results of different scenarios to explore the best management solutions that affect urban waterlogging resilience construction, specifically: Based on the existing parameter values, predict the changing trend of urban waterlogging resilience; Based on the existing historical data, by modifying the parameter values ​​in the mathematical function or modifying the behavioral design to set different scenario policies, the changing trends of urban waterlogging resilience in different scenarios are compared and analyzed, and suggestions are put forward for improving urban waterlogging resilience construction; Take practical measures based on the changing trends and suggestions to improve urban waterlogging resilience, thereby reducing the economic losses caused by waterlogging and improving the level of urban resilience.

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